import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
from ipywidgets import interact, widgets
import numpy as np
def load_data(data_dir):
"""Load cryptocurrency data into a dictionary."""
currency_data = {} currency_files = os.listdir(data_dir)
for file in currency_files:
if file.endswith('.csv'): currency_name = file[:-4]
try:
currency_data[currency_name] = pd.read_csv(os.path.join(data_dir, file), parse_dates=['date'])
except Exception as e:
print(f"Error loading {file}: {e}") return currency_data
data_dir = '/Users/darioperez/Documents/School/MSDS-570-01 - Fall 2024 Visual & Unstruct Data Analysi/Exercises/bit_project/bit_project/archive/'
currency_data = load_data(data_dir)
volatility = {}for ticker, df in currency_data.items():
if 'close' in df.columns:
volatility[ticker] = df['close'].std()
most_volatile_currencies = sorted(volatility.items(), key=lambda x: x[1], reverse=True)[:10]
most_volatile_currencies = dict(most_volatile_currencies)
num_currencies = len(most_volatile_currencies)
colors = plt.cm.tab10
color_list = [colors(i) for i in range(num_currencies)]
def plot_visualization(visualization_type):
if visualization_type == 'Horizontal Bar Chart':
plt.figure(figsize=(12, 6))
plt.barh(list(most_volatile_currencies.keys()), list(most_volatile_currencies.values()), color=color_list)
plt.title('Top 10 Most Volatile Cryptocurrencies Based on Closing Price Volatility', fontsize=16, fontweight='bold') plt.xlabel('Volatility (Standard Deviation of Closing Prices)', fontsize=14) plt.ylabel('Cryptocurrency', fontsize=14) plt.grid(True, axis='x', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()
elif visualization_type == 'Boxplot':
top_10_tickers = list(most_volatile_currencies.keys())
top_10_data = [currency_data[ticker]['close'] for ticker in top_10_tickers]
volatility_df = pd.DataFrame(top_10_data).T
volatility_df.columns = most_volatile_currencies.keys()
plt.figure(figsize=(12, 6))
sns.boxplot(data=volatility_df, palette='Set2')
plt.title('Volatility Distribution of Top 10 Most Volatile Cryptocurrencies', fontsize=16, fontweight='bold') plt.xlabel('Cryptocurrency', fontsize=14) plt.ylabel('Closing Prices', fontsize=14) plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
elif visualization_type == 'Scatter Plot (Volatility vs Avg Price)':
average_prices = {ticker: currency_data[ticker]['close'].mean() for ticker in most_volatile_currencies.keys()}
plt.figure(figsize=(12, 6))
plt.scatter(list(average_prices.values()), list(most_volatile_currencies.values()), color=color_list)
plt.title('Volatility vs. Average Closing Price for Top 10 Most Volatile Cryptocurrencies', fontsize=16, fontweight='bold') plt.xlabel('Average Closing Price', fontsize=14) plt.ylabel('Volatility (Standard Deviation)', fontsize=14) plt.tight_layout()
plt.show()
elif visualization_type == 'Heatmap of Volatility':
volatility_df = pd.DataFrame(list(most_volatile_currencies.items()), columns=['Cryptocurrency', 'Volatility'])
volatility_matrix = np.array(volatility_df['Volatility']).reshape(1, -1)
plt.figure(figsize=(12, 6))
sns.heatmap(volatility_matrix, annot=True, cmap='coolwarm', cbar=True, xticklabels=most_volatile_currencies.keys(), yticklabels=['Volatility'])
plt.title('Heatmap of Volatility for Top 10 Most Volatile Cryptocurrencies', fontsize=16, fontweight='bold') plt.xlabel('Cryptocurrency', fontsize=14) plt.ylabel('Volatility', fontsize=14) plt.tight_layout()
plt.show()
visualization_options = [
'Horizontal Bar Chart',
'Boxplot',
'Scatter Plot (Volatility vs Avg Price)',
'Heatmap of Volatility'
]
interact(plot_visualization, visualization_type=widgets.Dropdown(options=visualization_options, description='Visualization Type:'))
